Proteins communicate with each other using their constituent domains. Knowledge of domain-domain interactions will become part of a protein's functional annotation that is widely useful to the biomedical research community. Despite the availability of a vast amount of protein interaction data, our current knowledge on domain-domain interactions is very limited, because most of the protein-protein interaction data exist as `binary' data (i.e., interaction is either found or not found) that does not reveal which two domains are interacting. Determining all domain-domain interactions using experimental means is tedious and unfeasible. To take advantage of the vast amount of protein interaction data, computational methods can be exploited for inferring domain-domain interactions from protein-protein interaction data. Specific aims of this project are: (i) To develop a novel computational method for inferring biologically relevant domain-domain interactions from protein-protein interaction data, and (ii) To experimentally validate predicted interactions using yeast two-hybrid screens. In summary, a novel combination of scoring features will be employed in an integrated scoring algorithm, to accurately infer potential domain-domain interactions from the pool of all the theoretically possible interactions. Experimentally derived protein-protein interaction datasets from multiple sources and from multiple species will be utilized to ensure maximum coverage of domain- domain interactions. The performance of this method will be evaluated by testing the predictions against experimentally-known domain interactions in the iPfam database. Additionally, direct experimental validation of potential positive and negative interactions will be carried out for 100 interactions using the yeast two-hybrid screens. A selected list of 35 highest-scoring and 35 lowest-scoring predictions will be experimentally validated to assess the quality of these predictions. Additionally, we propose to test about 30 novel domain- domain interactions found in a core set of DNA repair proteins representing tumor suppressors. The methodology and data generated in this project can help understand the functional basis of protein-protein interactions, with a multitude of implications in systems biology research. [unreadable] [unreadable] [unreadable]